Instructions to use agadelmoula-avey/hf_tutorial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use agadelmoula-avey/hf_tutorial with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="agadelmoula-avey/hf_tutorial", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("agadelmoula-avey/hf_tutorial", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update modeling_avey.py
Browse files- modeling_avey.py +1 -1
modeling_avey.py
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@@ -12,7 +12,7 @@ from .configuration_avey import AveyConfig
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from torch.utils.checkpoint import checkpoint
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torch._dynamo.config.allow_unspec_int_on_nn_module = True
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class Contextualizer(nn.Module):
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def __init__(self, config: AveyConfig, layer_idx):
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from torch.utils.checkpoint import checkpoint
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# torch._dynamo.config.allow_unspec_int_on_nn_module = True
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class Contextualizer(nn.Module):
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def __init__(self, config: AveyConfig, layer_idx):
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